Next, since recurrent neural networks (RNNs) are able to well capture temporal dependency, we modify classic RNNs to capture spatio-temporal dependency. Specifically, we replace weight multiplications in classic RNNs with convolutions that take into account graph topology (e.g., graph convolution or diffusion convolution [1]}). However, instead of using a static adjacency matrix, we employ the attention matrix learned from the first step to obtain adaptive adjacency matrices. Here, with the learned attention weight matrices and the inputs at different timestamps, we obtain different adjacency matrices at different timestamps, which are able to capture the dynamic correlations among different time series at different timestamps.
